Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations32833
Missing cells15
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 MiB
Average record size in memory192.0 B

Variable types

Text7
Numeric13
DateTime1
Categorical3

Alerts

energy is highly overall correlated with loudnessHigh correlation
genre_clean is highly overall correlated with playlist_genre and 1 other fieldsHigh correlation
loudness is highly overall correlated with energyHigh correlation
playlist_genre is highly overall correlated with genre_clean and 1 other fieldsHigh correlation
playlist_subgenre is highly overall correlated with genre_clean and 1 other fieldsHigh correlation
track_popularity has 2703 (8.2%) zerosZeros
key has 3454 (10.5%) zerosZeros
instrumentalness has 12089 (36.8%) zerosZeros

Reproduction

Analysis started2024-09-20 09:18:58.479588
Analysis finished2024-09-20 09:19:24.332847
Duration25.85 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Distinct28356
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size256.6 KiB
2024-09-20T03:19:24.611589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters722326
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25190 ?
Unique (%)76.7%

Sample

1st row6f807x0ima9a1j3VPbc7VN
2nd row0r7CVbZTWZgbTCYdfa2P31
3rd row1z1Hg7Vb0AhHDiEmnDE79l
4th row75FpbthrwQmzHlBJLuGdC7
5th row1e8PAfcKUYoKkxPhrHqw4x
ValueCountFrequency (%)
7bklcz1jbubvqri2fvltvw 10
 
< 0.1%
14sos5l36385fj3ol8hew4 9
 
< 0.1%
3eekarcy7kvn4yt5zfzltw 9
 
< 0.1%
56amygjzxbo6p8v0wee9de 8
 
< 0.1%
6wo37kvqfjhtuxptplccfe 8
 
< 0.1%
6gg1gjgki2ak4e0qzsr7sd 8
 
< 0.1%
7h0d2h0fumzbs7zefigjpn 8
 
< 0.1%
0sf12qnh5qcw8qpgymfoqd 8
 
< 0.1%
2b8foow8uzydfae27yhozm 8
 
< 0.1%
7lzouawgfcy4tkxdooneym 8
 
< 0.1%
Other values (28346) 32749
99.7%
2024-09-20T03:19:25.193995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15445
 
2.1%
6 15375
 
2.1%
3 15304
 
2.1%
4 15295
 
2.1%
2 15212
 
2.1%
1 15190
 
2.1%
5 15170
 
2.1%
7 14649
 
2.0%
C 11345
 
1.6%
R 11338
 
1.6%
Other values (52) 578003
80.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 722326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15445
 
2.1%
6 15375
 
2.1%
3 15304
 
2.1%
4 15295
 
2.1%
2 15212
 
2.1%
1 15190
 
2.1%
5 15170
 
2.1%
7 14649
 
2.0%
C 11345
 
1.6%
R 11338
 
1.6%
Other values (52) 578003
80.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 722326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15445
 
2.1%
6 15375
 
2.1%
3 15304
 
2.1%
4 15295
 
2.1%
2 15212
 
2.1%
1 15190
 
2.1%
5 15170
 
2.1%
7 14649
 
2.0%
C 11345
 
1.6%
R 11338
 
1.6%
Other values (52) 578003
80.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 722326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15445
 
2.1%
6 15375
 
2.1%
3 15304
 
2.1%
4 15295
 
2.1%
2 15212
 
2.1%
1 15190
 
2.1%
5 15170
 
2.1%
7 14649
 
2.0%
C 11345
 
1.6%
R 11338
 
1.6%
Other values (52) 578003
80.0%
Distinct23449
Distinct (%)71.4%
Missing5
Missing (%)< 0.1%
Memory size256.6 KiB
2024-09-20T03:19:25.581048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length144
Median length95
Mean length17.266602
Min length1

Characters and Unicode

Total characters566828
Distinct characters418
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18468 ?
Unique (%)56.3%

Sample

1st rowI Don't Care (with Justin Bieber) - Loud Luxury Remix
2nd rowMemories - Dillon Francis Remix
3rd rowAll the Time - Don Diablo Remix
4th rowCall You Mine - Keanu Silva Remix
5th rowSomeone You Loved - Future Humans Remix
ValueCountFrequency (%)
6154
 
5.6%
feat 2804
 
2.6%
the 2681
 
2.5%
remix 2108
 
1.9%
you 1880
 
1.7%
me 1629
 
1.5%
i 1163
 
1.1%
love 1135
 
1.0%
a 850
 
0.8%
to 845
 
0.8%
Other values (15594) 88122
80.6%
2024-09-20T03:19:26.219104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
76543
 
13.5%
e 51413
 
9.1%
a 34941
 
6.2%
o 33285
 
5.9%
i 30273
 
5.3%
t 25793
 
4.6%
n 25222
 
4.4%
r 22418
 
4.0%
l 17202
 
3.0%
s 16539
 
2.9%
Other values (408) 233199
41.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 566828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
76543
 
13.5%
e 51413
 
9.1%
a 34941
 
6.2%
o 33285
 
5.9%
i 30273
 
5.3%
t 25793
 
4.6%
n 25222
 
4.4%
r 22418
 
4.0%
l 17202
 
3.0%
s 16539
 
2.9%
Other values (408) 233199
41.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 566828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
76543
 
13.5%
e 51413
 
9.1%
a 34941
 
6.2%
o 33285
 
5.9%
i 30273
 
5.3%
t 25793
 
4.6%
n 25222
 
4.4%
r 22418
 
4.0%
l 17202
 
3.0%
s 16539
 
2.9%
Other values (408) 233199
41.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 566828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
76543
 
13.5%
e 51413
 
9.1%
a 34941
 
6.2%
o 33285
 
5.9%
i 30273
 
5.3%
t 25793
 
4.6%
n 25222
 
4.4%
r 22418
 
4.0%
l 17202
 
3.0%
s 16539
 
2.9%
Other values (408) 233199
41.1%
Distinct10692
Distinct (%)32.6%
Missing5
Missing (%)< 0.1%
Memory size256.6 KiB
2024-09-20T03:19:26.675143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length69
Median length40
Mean length10.048495
Min length2

Characters and Unicode

Total characters329872
Distinct characters216
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6141 ?
Unique (%)18.7%

Sample

1st rowEd Sheeran
2nd rowMaroon 5
3rd rowZara Larsson
4th rowThe Chainsmokers
5th rowLewis Capaldi
ValueCountFrequency (%)
the 1774
 
3.0%
778
 
1.3%
dj 236
 
0.4%
martin 229
 
0.4%
mike 221
 
0.4%
lil 218
 
0.4%
j 189
 
0.3%
david 178
 
0.3%
of 171
 
0.3%
garrix 162
 
0.3%
Other values (11585) 54168
92.9%
2024-09-20T03:19:27.465194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 28721
 
8.7%
a 26552
 
8.0%
25496
 
7.7%
i 19360
 
5.9%
n 18592
 
5.6%
o 18495
 
5.6%
r 16806
 
5.1%
l 13959
 
4.2%
s 12188
 
3.7%
t 11038
 
3.3%
Other values (206) 138665
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 329872
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 28721
 
8.7%
a 26552
 
8.0%
25496
 
7.7%
i 19360
 
5.9%
n 18592
 
5.6%
o 18495
 
5.6%
r 16806
 
5.1%
l 13959
 
4.2%
s 12188
 
3.7%
t 11038
 
3.3%
Other values (206) 138665
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 329872
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 28721
 
8.7%
a 26552
 
8.0%
25496
 
7.7%
i 19360
 
5.9%
n 18592
 
5.6%
o 18495
 
5.6%
r 16806
 
5.1%
l 13959
 
4.2%
s 12188
 
3.7%
t 11038
 
3.3%
Other values (206) 138665
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 329872
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 28721
 
8.7%
a 26552
 
8.0%
25496
 
7.7%
i 19360
 
5.9%
n 18592
 
5.6%
o 18495
 
5.6%
r 16806
 
5.1%
l 13959
 
4.2%
s 12188
 
3.7%
t 11038
 
3.3%
Other values (206) 138665
42.0%

track_popularity
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.477081
Minimum0
Maximum100
Zeros2703
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-09-20T03:19:27.651394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124
median45
Q362
95-th percentile79
Maximum100
Range100
Interquartile range (IQR)38

Descriptive statistics

Standard deviation24.984074
Coefficient of variation (CV)0.58817776
Kurtosis-0.93277039
Mean42.477081
Median Absolute Deviation (MAD)18
Skewness-0.23332007
Sum1394650
Variance624.20398
MonotonicityNot monotonic
2024-09-20T03:19:27.852888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2703
 
8.2%
1 575
 
1.8%
57 541
 
1.6%
51 514
 
1.6%
60 514
 
1.6%
54 514
 
1.6%
52 506
 
1.5%
45 505
 
1.5%
58 503
 
1.5%
50 498
 
1.5%
Other values (91) 25460
77.5%
ValueCountFrequency (%)
0 2703
8.2%
1 575
 
1.8%
2 387
 
1.2%
3 321
 
1.0%
4 240
 
0.7%
5 240
 
0.7%
6 192
 
0.6%
7 189
 
0.6%
8 201
 
0.6%
9 195
 
0.6%
ValueCountFrequency (%)
100 2
 
< 0.1%
99 4
 
< 0.1%
98 36
0.1%
97 22
 
0.1%
96 7
 
< 0.1%
95 15
 
< 0.1%
94 37
0.1%
93 44
0.1%
92 27
0.1%
91 58
0.2%
Distinct22545
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Memory size256.6 KiB
2024-09-20T03:19:28.195838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters722326
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17545 ?
Unique (%)53.4%

Sample

1st row2oCs0DGTsRO98Gh5ZSl2Cx
2nd row63rPSO264uRjW1X5E6cWv6
3rd row1HoSmj2eLcsrR0vE9gThr4
4th row1nqYsOef1yKKuGOVchbsk6
5th row7m7vv9wlQ4i0LFuJiE2zsQ
ValueCountFrequency (%)
5l1xcowsxwzfusjzvymp48 42
 
0.1%
5fstcqs5npilf42vhpnv23 29
 
0.1%
7cjjb2mikwawa1v6kewfbf 28
 
0.1%
4vfg1doutedmbjblzt7hck 26
 
0.1%
4czt5uefbrpbilw34hqyxi 21
 
0.1%
2htbq0rhwukkvxaltmczp2 21
 
0.1%
246e5ovv4qxhprgosj7vdb 20
 
0.1%
6ylffzx32icw4l1a7ywnln 20
 
0.1%
5xcotqg63v60ns82pmqmbe 20
 
0.1%
0s0kgznfbgsissff54wsjh 18
 
0.1%
Other values (22535) 32588
99.3%
2024-09-20T03:19:28.734664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 15957
 
2.2%
2 15554
 
2.2%
0 15513
 
2.1%
5 15509
 
2.1%
3 15467
 
2.1%
6 15368
 
2.1%
4 15075
 
2.1%
7 14325
 
2.0%
w 11443
 
1.6%
e 11355
 
1.6%
Other values (52) 576760
79.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 722326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 15957
 
2.2%
2 15554
 
2.2%
0 15513
 
2.1%
5 15509
 
2.1%
3 15467
 
2.1%
6 15368
 
2.1%
4 15075
 
2.1%
7 14325
 
2.0%
w 11443
 
1.6%
e 11355
 
1.6%
Other values (52) 576760
79.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 722326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 15957
 
2.2%
2 15554
 
2.2%
0 15513
 
2.1%
5 15509
 
2.1%
3 15467
 
2.1%
6 15368
 
2.1%
4 15075
 
2.1%
7 14325
 
2.0%
w 11443
 
1.6%
e 11355
 
1.6%
Other values (52) 576760
79.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 722326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 15957
 
2.2%
2 15554
 
2.2%
0 15513
 
2.1%
5 15509
 
2.1%
3 15467
 
2.1%
6 15368
 
2.1%
4 15075
 
2.1%
7 14325
 
2.0%
w 11443
 
1.6%
e 11355
 
1.6%
Other values (52) 576760
79.8%
Distinct19743
Distinct (%)60.1%
Missing5
Missing (%)< 0.1%
Memory size256.6 KiB
2024-09-20T03:19:29.166257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length151
Median length102
Mean length17.494547
Min length1

Characters and Unicode

Total characters574311
Distinct characters346
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14210 ?
Unique (%)43.3%

Sample

1st rowI Don't Care (with Justin Bieber) [Loud Luxury Remix]
2nd rowMemories (Dillon Francis Remix)
3rd rowAll the Time (Don Diablo Remix)
4th rowCall You Mine - The Remixes
5th rowSomeone You Loved (Future Humans Remix)
ValueCountFrequency (%)
the 4566
 
4.5%
1928
 
1.9%
of 1576
 
1.5%
feat 1398
 
1.4%
remix 1119
 
1.1%
you 1064
 
1.0%
me 929
 
0.9%
deluxe 914
 
0.9%
a 837
 
0.8%
love 795
 
0.8%
Other values (14469) 87117
85.2%
2024-09-20T03:19:29.766742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
69415
 
12.1%
e 54490
 
9.5%
o 32559
 
5.7%
a 31919
 
5.6%
i 30661
 
5.3%
t 26691
 
4.6%
n 25723
 
4.5%
r 24106
 
4.2%
s 21005
 
3.7%
l 19252
 
3.4%
Other values (336) 238490
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 574311
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
69415
 
12.1%
e 54490
 
9.5%
o 32559
 
5.7%
a 31919
 
5.6%
i 30661
 
5.3%
t 26691
 
4.6%
n 25723
 
4.5%
r 24106
 
4.2%
s 21005
 
3.7%
l 19252
 
3.4%
Other values (336) 238490
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 574311
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
69415
 
12.1%
e 54490
 
9.5%
o 32559
 
5.7%
a 31919
 
5.6%
i 30661
 
5.3%
t 26691
 
4.6%
n 25723
 
4.5%
r 24106
 
4.2%
s 21005
 
3.7%
l 19252
 
3.4%
Other values (336) 238490
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 574311
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
69415
 
12.1%
e 54490
 
9.5%
o 32559
 
5.7%
a 31919
 
5.6%
i 30661
 
5.3%
t 26691
 
4.6%
n 25723
 
4.5%
r 24106
 
4.2%
s 21005
 
3.7%
l 19252
 
3.4%
Other values (336) 238490
41.5%
Distinct4474
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size256.6 KiB
Minimum1957-01-01 00:00:00
Maximum2020-01-29 00:00:00
2024-09-20T03:19:29.932189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:30.149661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct449
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size256.6 KiB
2024-09-20T03:19:30.489353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length120
Median length82
Mean length24.835196
Min length6

Characters and Unicode

Total characters815414
Distinct characters154
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPop Remix
2nd rowPop Remix
3rd rowPop Remix
4th rowPop Remix
5th rowPop Remix
ValueCountFrequency (%)
12734
 
9.0%
pop 4949
 
3.5%
rock 4706
 
3.3%
house 3404
 
2.4%
2020 2499
 
1.8%
hip 2448
 
1.7%
hits 2278
 
1.6%
rap 2168
 
1.5%
hop 2095
 
1.5%
edm 1997
 
1.4%
Other values (640) 102841
72.4%
2024-09-20T03:19:31.448352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
111011
 
13.6%
o 53679
 
6.6%
e 46675
 
5.7%
a 39825
 
4.9%
s 39279
 
4.8%
i 37137
 
4.6%
n 29115
 
3.6%
t 28876
 
3.5%
r 28395
 
3.5%
p 28050
 
3.4%
Other values (144) 373372
45.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815414
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
111011
 
13.6%
o 53679
 
6.6%
e 46675
 
5.7%
a 39825
 
4.9%
s 39279
 
4.8%
i 37137
 
4.6%
n 29115
 
3.6%
t 28876
 
3.5%
r 28395
 
3.5%
p 28050
 
3.4%
Other values (144) 373372
45.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815414
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
111011
 
13.6%
o 53679
 
6.6%
e 46675
 
5.7%
a 39825
 
4.9%
s 39279
 
4.8%
i 37137
 
4.6%
n 29115
 
3.6%
t 28876
 
3.5%
r 28395
 
3.5%
p 28050
 
3.4%
Other values (144) 373372
45.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815414
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
111011
 
13.6%
o 53679
 
6.6%
e 46675
 
5.7%
a 39825
 
4.9%
s 39279
 
4.8%
i 37137
 
4.6%
n 29115
 
3.6%
t 28876
 
3.5%
r 28395
 
3.5%
p 28050
 
3.4%
Other values (144) 373372
45.8%
Distinct471
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size256.6 KiB
2024-09-20T03:19:31.810153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters722326
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row37i9dQZF1DXcZDD7cfEKhW
2nd row37i9dQZF1DXcZDD7cfEKhW
3rd row37i9dQZF1DXcZDD7cfEKhW
4th row37i9dQZF1DXcZDD7cfEKhW
5th row37i9dQZF1DXcZDD7cfEKhW
ValueCountFrequency (%)
4jkkvmpvl4lsioqqjeal0q 247
 
0.8%
37i9dqzf1dwthm4kx49uks 198
 
0.6%
6knqdwp0syvhfhor4lwp7x 195
 
0.6%
3xmqtdloigvj3lwh5e5x6f 189
 
0.6%
3ho3io0ijykgeqnbjb2sic 182
 
0.6%
25butzrvb1zj1mjioms09d 109
 
0.3%
0jmbb9hfrzdizopvrdv8ns 100
 
0.3%
1eqvgsnjax6mxdpoefhoct 100
 
0.3%
1s7bckuyikeazenkosm0ua 100
 
0.3%
5ck0fshhcik1vwyecc0zat 100
 
0.3%
Other values (461) 31313
95.4%
2024-09-20T03:19:32.362904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 19178
 
2.7%
1 18762
 
2.6%
7 18699
 
2.6%
Q 18247
 
2.5%
D 17097
 
2.4%
i 17039
 
2.4%
d 16004
 
2.2%
9 15566
 
2.2%
Z 15453
 
2.1%
F 15033
 
2.1%
Other values (52) 551248
76.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 722326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 19178
 
2.7%
1 18762
 
2.6%
7 18699
 
2.6%
Q 18247
 
2.5%
D 17097
 
2.4%
i 17039
 
2.4%
d 16004
 
2.2%
9 15566
 
2.2%
Z 15453
 
2.1%
F 15033
 
2.1%
Other values (52) 551248
76.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 722326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 19178
 
2.7%
1 18762
 
2.6%
7 18699
 
2.6%
Q 18247
 
2.5%
D 17097
 
2.4%
i 17039
 
2.4%
d 16004
 
2.2%
9 15566
 
2.2%
Z 15453
 
2.1%
F 15033
 
2.1%
Other values (52) 551248
76.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 722326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 19178
 
2.7%
1 18762
 
2.6%
7 18699
 
2.6%
Q 18247
 
2.5%
D 17097
 
2.4%
i 17039
 
2.4%
d 16004
 
2.2%
9 15566
 
2.2%
Z 15453
 
2.1%
F 15033
 
2.1%
Other values (52) 551248
76.3%

playlist_genre
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size256.6 KiB
edm
6043 
rap
5746 
pop
5507 
r&b
5431 
latin
5155 

Length

Max length5
Median length3
Mean length3.4648067
Min length3

Characters and Unicode

Total characters113760
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpop
2nd rowpop
3rd rowpop
4th rowpop
5th rowpop

Common Values

ValueCountFrequency (%)
edm 6043
18.4%
rap 5746
17.5%
pop 5507
16.8%
r&b 5431
16.5%
latin 5155
15.7%
rock 4951
15.1%

Length

2024-09-20T03:19:32.667174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T03:19:32.910136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
edm 6043
18.4%
rap 5746
17.5%
pop 5507
16.8%
r&b 5431
16.5%
latin 5155
15.7%
rock 4951
15.1%

Most occurring characters

ValueCountFrequency (%)
p 16760
14.7%
r 16128
14.2%
a 10901
9.6%
o 10458
9.2%
m 6043
 
5.3%
e 6043
 
5.3%
d 6043
 
5.3%
& 5431
 
4.8%
b 5431
 
4.8%
l 5155
 
4.5%
Other values (5) 25367
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 16760
14.7%
r 16128
14.2%
a 10901
9.6%
o 10458
9.2%
m 6043
 
5.3%
e 6043
 
5.3%
d 6043
 
5.3%
& 5431
 
4.8%
b 5431
 
4.8%
l 5155
 
4.5%
Other values (5) 25367
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 16760
14.7%
r 16128
14.2%
a 10901
9.6%
o 10458
9.2%
m 6043
 
5.3%
e 6043
 
5.3%
d 6043
 
5.3%
& 5431
 
4.8%
b 5431
 
4.8%
l 5155
 
4.5%
Other values (5) 25367
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 16760
14.7%
r 16128
14.2%
a 10901
9.6%
o 10458
9.2%
m 6043
 
5.3%
e 6043
 
5.3%
d 6043
 
5.3%
& 5431
 
4.8%
b 5431
 
4.8%
l 5155
 
4.5%
Other values (5) 25367
22.3%

playlist_subgenre
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size256.6 KiB
progressive electro house
 
1809
southern hip hop
 
1675
indie poptimism
 
1672
latin hip hop
 
1656
neo soul
 
1637
Other values (19)
24384 

Length

Max length25
Median length15
Mean length11.548625
Min length4

Characters and Unicode

Total characters379176
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdance pop
2nd rowdance pop
3rd rowdance pop
4th rowdance pop
5th rowdance pop

Common Values

ValueCountFrequency (%)
progressive electro house 1809
 
5.5%
southern hip hop 1675
 
5.1%
indie poptimism 1672
 
5.1%
latin hip hop 1656
 
5.0%
neo soul 1637
 
5.0%
pop edm 1517
 
4.6%
electro house 1511
 
4.6%
hard rock 1485
 
4.5%
gangster rap 1458
 
4.4%
electropop 1408
 
4.3%
Other values (14) 17005
51.8%

Length

2024-09-20T03:19:33.107579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pop 6462
 
9.6%
hip 5909
 
8.8%
hop 4653
 
6.9%
rock 3846
 
5.7%
house 3320
 
5.0%
electro 3320
 
5.0%
latin 2918
 
4.4%
progressive 1809
 
2.7%
southern 1675
 
2.5%
indie 1672
 
2.5%
Other values (24) 31419
46.9%

Most occurring characters

ValueCountFrequency (%)
o 41435
10.9%
p 39131
 
10.3%
e 35660
 
9.4%
34170
 
9.0%
r 28322
 
7.5%
i 22247
 
5.9%
t 20747
 
5.5%
a 20659
 
5.4%
n 20022
 
5.3%
s 18234
 
4.8%
Other values (14) 98549
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 379176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 41435
10.9%
p 39131
 
10.3%
e 35660
 
9.4%
34170
 
9.0%
r 28322
 
7.5%
i 22247
 
5.9%
t 20747
 
5.5%
a 20659
 
5.4%
n 20022
 
5.3%
s 18234
 
4.8%
Other values (14) 98549
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 379176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 41435
10.9%
p 39131
 
10.3%
e 35660
 
9.4%
34170
 
9.0%
r 28322
 
7.5%
i 22247
 
5.9%
t 20747
 
5.5%
a 20659
 
5.4%
n 20022
 
5.3%
s 18234
 
4.8%
Other values (14) 98549
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 379176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 41435
10.9%
p 39131
 
10.3%
e 35660
 
9.4%
34170
 
9.0%
r 28322
 
7.5%
i 22247
 
5.9%
t 20747
 
5.5%
a 20659
 
5.4%
n 20022
 
5.3%
s 18234
 
4.8%
Other values (14) 98549
26.0%

danceability
Real number (ℝ)

Distinct822
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65484952
Minimum0
Maximum0.983
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-09-20T03:19:33.305275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.392
Q10.563
median0.672
Q30.761
95-th percentile0.868
Maximum0.983
Range0.983
Interquartile range (IQR)0.198

Descriptive statistics

Standard deviation0.14508532
Coefficient of variation (CV)0.22155521
Kurtosis0.010202119
Mean0.65484952
Median Absolute Deviation (MAD)0.098
Skewness-0.50448844
Sum21500.674
Variance0.02104975
MonotonicityNot monotonic
2024-09-20T03:19:33.779608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.733 118
 
0.4%
0.708 115
 
0.4%
0.704 112
 
0.3%
0.694 112
 
0.3%
0.784 111
 
0.3%
0.701 111
 
0.3%
0.69 111
 
0.3%
0.655 110
 
0.3%
0.676 110
 
0.3%
0.689 109
 
0.3%
Other values (812) 31714
96.6%
ValueCountFrequency (%)
0 1
< 0.1%
0.0771 1
< 0.1%
0.0787 1
< 0.1%
0.0985 1
< 0.1%
0.116 1
< 0.1%
0.118 1
< 0.1%
0.13 1
< 0.1%
0.135 2
< 0.1%
0.14 2
< 0.1%
0.141 1
< 0.1%
ValueCountFrequency (%)
0.983 1
 
< 0.1%
0.981 1
 
< 0.1%
0.979 2
 
< 0.1%
0.978 1
 
< 0.1%
0.977 1
 
< 0.1%
0.975 2
 
< 0.1%
0.974 5
< 0.1%
0.973 4
< 0.1%
0.972 2
 
< 0.1%
0.971 2
 
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct952
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69861927
Minimum0.000175
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-09-20T03:19:33.960080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.000175
5-th percentile0.366
Q10.581
median0.721
Q30.84
95-th percentile0.949
Maximum1
Range0.999825
Interquartile range (IQR)0.259

Descriptive statistics

Standard deviation0.18091003
Coefficient of variation (CV)0.25895368
Kurtosis0.000528152
Mean0.69861927
Median Absolute Deviation (MAD)0.128
Skewness-0.63632984
Sum22937.767
Variance0.03272844
MonotonicityNot monotonic
2024-09-20T03:19:34.144221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.787 100
 
0.3%
0.828 99
 
0.3%
0.833 98
 
0.3%
0.726 91
 
0.3%
0.795 91
 
0.3%
0.711 91
 
0.3%
0.869 89
 
0.3%
0.758 89
 
0.3%
0.76 88
 
0.3%
0.887 87
 
0.3%
Other values (942) 31910
97.2%
ValueCountFrequency (%)
0.000175 1
< 0.1%
0.00814 1
< 0.1%
0.0118 1
< 0.1%
0.0161 1
< 0.1%
0.0167 1
< 0.1%
0.0286 1
< 0.1%
0.0297 1
< 0.1%
0.0323 1
< 0.1%
0.036 1
< 0.1%
0.0375 1
< 0.1%
ValueCountFrequency (%)
1 3
 
< 0.1%
0.999 7
 
< 0.1%
0.998 5
 
< 0.1%
0.997 6
 
< 0.1%
0.996 10
 
< 0.1%
0.995 13
< 0.1%
0.994 11
 
< 0.1%
0.993 30
0.1%
0.992 17
0.1%
0.991 20
0.1%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3744708
Minimum0
Maximum11
Zeros3454
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-09-20T03:19:34.292789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.6116574
Coefficient of variation (CV)0.67200242
Kurtosis-1.307069
Mean5.3744708
Median Absolute Deviation (MAD)3
Skewness-0.023909144
Sum176460
Variance13.044069
MonotonicityNot monotonic
2024-09-20T03:19:34.423293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 4010
12.2%
0 3454
10.5%
7 3352
10.2%
9 3027
9.2%
11 2996
9.1%
2 2827
8.6%
5 2680
8.2%
6 2670
8.1%
8 2430
7.4%
10 2273
6.9%
Other values (2) 3114
9.5%
ValueCountFrequency (%)
0 3454
10.5%
1 4010
12.2%
2 2827
8.6%
3 913
 
2.8%
4 2201
6.7%
5 2680
8.2%
6 2670
8.1%
7 3352
10.2%
8 2430
7.4%
9 3027
9.2%
ValueCountFrequency (%)
11 2996
9.1%
10 2273
6.9%
9 3027
9.2%
8 2430
7.4%
7 3352
10.2%
6 2670
8.1%
5 2680
8.2%
4 2201
6.7%
3 913
 
2.8%
2 2827
8.6%

loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct10222
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.7194991
Minimum-46.448
Maximum1.275
Zeros0
Zeros (%)0.0%
Negative32827
Negative (%)> 99.9%
Memory size256.6 KiB
2024-09-20T03:19:34.583491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-46.448
5-th percentile-12.4502
Q1-8.171
median-6.166
Q3-4.645
95-th percentile-2.972
Maximum1.275
Range47.723
Interquartile range (IQR)3.526

Descriptive statistics

Standard deviation2.9884364
Coefficient of variation (CV)-0.44474094
Kurtosis4.4909579
Mean-6.7194991
Median Absolute Deviation (MAD)1.703
Skewness-1.364097
Sum-220621.32
Variance8.930752
MonotonicityNot monotonic
2024-09-20T03:19:34.745983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.443 20
 
0.1%
-5.608 20
 
0.1%
-4.973 20
 
0.1%
-3.782 20
 
0.1%
-6.4 20
 
0.1%
-5.576 18
 
0.1%
-4.576 18
 
0.1%
-6.406 18
 
0.1%
-5.041 18
 
0.1%
-6.554 16
 
< 0.1%
Other values (10212) 32645
99.4%
ValueCountFrequency (%)
-46.448 1
< 0.1%
-36.624 1
< 0.1%
-36.509 1
< 0.1%
-35.96 1
< 0.1%
-35.427 1
< 0.1%
-34.283 1
< 0.1%
-29.561 1
< 0.1%
-28.309 1
< 0.1%
-26.279 1
< 0.1%
-26.207 1
< 0.1%
ValueCountFrequency (%)
1.275 1
< 0.1%
1.135 1
< 0.1%
0.642 1
< 0.1%
0.551 1
< 0.1%
0.326 1
< 0.1%
0.302 1
< 0.1%
-0.046 1
< 0.1%
-0.073 1
< 0.1%
-0.155 1
< 0.1%
-0.158 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size256.6 KiB
1
18574 
0
14259 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32833
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 18574
56.6%
0 14259
43.4%

Length

2024-09-20T03:19:34.913425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T03:19:35.029872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 18574
56.6%
0 14259
43.4%

Most occurring characters

ValueCountFrequency (%)
1 18574
56.6%
0 14259
43.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32833
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 18574
56.6%
0 14259
43.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32833
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 18574
56.6%
0 14259
43.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32833
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 18574
56.6%
0 14259
43.4%

speechiness
Real number (ℝ)

Distinct1270
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10706807
Minimum0
Maximum0.918
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-09-20T03:19:35.172799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0298
Q10.041
median0.0625
Q30.132
95-th percentile0.3324
Maximum0.918
Range0.918
Interquartile range (IQR)0.091

Descriptive statistics

Standard deviation0.10131413
Coefficient of variation (CV)0.94625907
Kurtosis4.2608346
Mean0.10706807
Median Absolute Deviation (MAD)0.0274
Skewness1.9670285
Sum3515.3659
Variance0.010264553
MonotonicityNot monotonic
2024-09-20T03:19:35.340012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.102 116
 
0.4%
0.103 98
 
0.3%
0.109 93
 
0.3%
0.0354 93
 
0.3%
0.112 89
 
0.3%
0.0346 88
 
0.3%
0.107 88
 
0.3%
0.123 87
 
0.3%
0.106 85
 
0.3%
0.0363 85
 
0.3%
Other values (1260) 31911
97.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.0224 2
 
< 0.1%
0.0225 1
 
< 0.1%
0.0228 5
< 0.1%
0.023 1
 
< 0.1%
0.0231 1
 
< 0.1%
0.0232 4
< 0.1%
0.0233 2
 
< 0.1%
0.0234 3
< 0.1%
0.0235 6
< 0.1%
ValueCountFrequency (%)
0.918 1
< 0.1%
0.877 1
< 0.1%
0.869 2
< 0.1%
0.865 1
< 0.1%
0.86 1
< 0.1%
0.856 1
< 0.1%
0.855 1
< 0.1%
0.853 1
< 0.1%
0.817 1
< 0.1%
0.792 1
< 0.1%

acousticness
Real number (ℝ)

Distinct3731
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17533372
Minimum0
Maximum0.994
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-09-20T03:19:35.506552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0006306
Q10.0151
median0.0804
Q30.255
95-th percentile0.682
Maximum0.994
Range0.994
Interquartile range (IQR)0.2399

Descriptive statistics

Standard deviation0.21963254
Coefficient of variation (CV)1.2526543
Kurtosis1.8784089
Mean0.17533372
Median Absolute Deviation (MAD)0.07623
Skewness1.5947859
Sum5756.7319
Variance0.048238453
MonotonicityNot monotonic
2024-09-20T03:19:35.673179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.102 80
 
0.2%
0.128 80
 
0.2%
0.101 78
 
0.2%
0.114 74
 
0.2%
0.141 71
 
0.2%
0.107 70
 
0.2%
0.125 69
 
0.2%
0.104 69
 
0.2%
0.122 68
 
0.2%
0.11 65
 
0.2%
Other values (3721) 32109
97.8%
ValueCountFrequency (%)
0 1
< 0.1%
1.4 × 10-61
< 0.1%
1.44 × 10-61
< 0.1%
1.47 × 10-61
< 0.1%
1.66 × 10-61
< 0.1%
2.16 × 10-61
< 0.1%
2.22 × 10-61
< 0.1%
2.32 × 10-61
< 0.1%
2.43 × 10-61
< 0.1%
2.46 × 10-61
< 0.1%
ValueCountFrequency (%)
0.994 1
 
< 0.1%
0.992 1
 
< 0.1%
0.989 3
< 0.1%
0.986 2
< 0.1%
0.985 2
< 0.1%
0.984 2
< 0.1%
0.983 3
< 0.1%
0.982 1
 
< 0.1%
0.979 4
< 0.1%
0.978 3
< 0.1%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct4729
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.084747161
Minimum0
Maximum0.994
Zeros12089
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-09-20T03:19:35.837745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.61 × 10-5
Q30.00483
95-th percentile0.767
Maximum0.994
Range0.994
Interquartile range (IQR)0.00483

Descriptive statistics

Standard deviation0.22423012
Coefficient of variation (CV)2.6458718
Kurtosis6.2740615
Mean0.084747161
Median Absolute Deviation (MAD)1.61 × 10-5
Skewness2.7594718
Sum2782.5035
Variance0.050279149
MonotonicityNot monotonic
2024-09-20T03:19:36.009670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12089
36.8%
0.124 30
 
0.1%
0.00106 30
 
0.1%
1.16 × 10-626
 
0.1%
1.21 × 10-626
 
0.1%
1.17 × 10-523
 
0.1%
0.00016 23
 
0.1%
0.000115 22
 
0.1%
1.85 × 10-522
 
0.1%
0.0114 22
 
0.1%
Other values (4719) 20520
62.5%
ValueCountFrequency (%)
0 12089
36.8%
1 × 10-65
 
< 0.1%
1.01 × 10-617
 
0.1%
1.02 × 10-67
 
< 0.1%
1.03 × 10-614
 
< 0.1%
1.04 × 10-620
 
0.1%
1.05 × 10-69
 
< 0.1%
1.06 × 10-610
 
< 0.1%
1.07 × 10-613
 
< 0.1%
1.08 × 10-613
 
< 0.1%
ValueCountFrequency (%)
0.994 2
< 0.1%
0.987 1
 
< 0.1%
0.983 1
 
< 0.1%
0.982 1
 
< 0.1%
0.981 1
 
< 0.1%
0.979 1
 
< 0.1%
0.974 2
< 0.1%
0.972 3
< 0.1%
0.971 2
< 0.1%
0.97 1
 
< 0.1%

liveness
Real number (ℝ)

Distinct1624
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1901762
Minimum0
Maximum0.996
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-09-20T03:19:36.187847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0559
Q10.0927
median0.127
Q30.248
95-th percentile0.5104
Maximum0.996
Range0.996
Interquartile range (IQR)0.1553

Descriptive statistics

Standard deviation0.15431728
Coefficient of variation (CV)0.81144372
Kurtosis5.065937
Mean0.1901762
Median Absolute Deviation (MAD)0.0496
Skewness2.0767204
Sum6244.055
Variance0.023813823
MonotonicityNot monotonic
2024-09-20T03:19:36.359355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 346
 
1.1%
0.108 310
 
0.9%
0.11 305
 
0.9%
0.105 295
 
0.9%
0.104 294
 
0.9%
0.109 287
 
0.9%
0.106 284
 
0.9%
0.101 275
 
0.8%
0.112 272
 
0.8%
0.107 266
 
0.8%
Other values (1614) 29899
91.1%
ValueCountFrequency (%)
0 1
< 0.1%
0.00936 1
< 0.1%
0.00946 1
< 0.1%
0.0131 1
< 0.1%
0.015 2
< 0.1%
0.0155 2
< 0.1%
0.0158 1
< 0.1%
0.0163 1
< 0.1%
0.0165 1
< 0.1%
0.0167 1
< 0.1%
ValueCountFrequency (%)
0.996 1
 
< 0.1%
0.994 1
 
< 0.1%
0.992 1
 
< 0.1%
0.991 2
 
< 0.1%
0.99 3
< 0.1%
0.988 5
< 0.1%
0.985 3
< 0.1%
0.984 1
 
< 0.1%
0.983 2
 
< 0.1%
0.982 1
 
< 0.1%

valence
Real number (ℝ)

Distinct1362
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51056097
Minimum0
Maximum0.991
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-09-20T03:19:36.558089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.132
Q10.331
median0.512
Q30.693
95-th percentile0.893
Maximum0.991
Range0.991
Interquartile range (IQR)0.362

Descriptive statistics

Standard deviation0.23314597
Coefficient of variation (CV)0.45664668
Kurtosis-0.90098076
Mean0.51056097
Median Absolute Deviation (MAD)0.181
Skewness-0.0054853502
Sum16763.248
Variance0.054357045
MonotonicityNot monotonic
2024-09-20T03:19:36.789042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 69
 
0.2%
0.58 68
 
0.2%
0.389 68
 
0.2%
0.499 68
 
0.2%
0.562 68
 
0.2%
0.43 68
 
0.2%
0.516 67
 
0.2%
0.347 66
 
0.2%
0.392 66
 
0.2%
0.536 66
 
0.2%
Other values (1352) 32159
97.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 × 10-55
< 0.1%
0.0116 1
 
< 0.1%
0.0122 1
 
< 0.1%
0.0139 1
 
< 0.1%
0.0159 1
 
< 0.1%
0.0223 1
 
< 0.1%
0.0234 1
 
< 0.1%
0.0269 1
 
< 0.1%
0.0276 1
 
< 0.1%
ValueCountFrequency (%)
0.991 1
 
< 0.1%
0.99 1
 
< 0.1%
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 1
 
< 0.1%
0.981 2
 
< 0.1%
0.98 1
 
< 0.1%
0.979 3
< 0.1%
0.978 1
 
< 0.1%
0.977 5
< 0.1%

tempo
Real number (ℝ)

Distinct17684
Distinct (%)53.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.88113
Minimum0
Maximum239.44
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-09-20T03:19:37.009121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile81.0682
Q199.96
median121.984
Q3133.918
95-th percentile173.95
Maximum239.44
Range239.44
Interquartile range (IQR)33.958

Descriptive statistics

Standard deviation26.903624
Coefficient of variation (CV)0.22256264
Kurtosis0.08326436
Mean120.88113
Median Absolute Deviation (MAD)18.045
Skewness0.52887789
Sum3968890.2
Variance723.80499
MonotonicityNot monotonic
2024-09-20T03:19:37.227726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.992 45
 
0.1%
127.994 35
 
0.1%
127.993 33
 
0.1%
128.007 32
 
0.1%
127.997 31
 
0.1%
128.003 31
 
0.1%
128.001 31
 
0.1%
128.005 30
 
0.1%
128.017 29
 
0.1%
127.991 29
 
0.1%
Other values (17674) 32507
99.0%
ValueCountFrequency (%)
0 1
< 0.1%
35.477 1
< 0.1%
37.114 1
< 0.1%
38.985 1
< 0.1%
46.169 1
< 0.1%
48.718 2
< 0.1%
48.981 1
< 0.1%
49.597 1
< 0.1%
50.454 1
< 0.1%
52.017 1
< 0.1%
ValueCountFrequency (%)
239.44 1
< 0.1%
220.252 1
< 0.1%
219.991 1
< 0.1%
219.961 1
< 0.1%
214.516 1
< 0.1%
214.047 1
< 0.1%
214.017 1
< 0.1%
213.99 1
< 0.1%
212.137 2
< 0.1%
212.058 1
< 0.1%

duration_ms
Real number (ℝ)

Distinct19785
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225799.81
Minimum4000
Maximum517810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-09-20T03:19:37.458036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile148394.8
Q1187819
median216000
Q3253585
95-th percentile337400
Maximum517810
Range513810
Interquartile range (IQR)65766

Descriptive statistics

Standard deviation59834.006
Coefficient of variation (CV)0.26498696
Kurtosis2.6991863
Mean225799.81
Median Absolute Deviation (MAD)31867
Skewness1.1498633
Sum7.4136852 × 109
Variance3.5801083 × 109
MonotonicityNot monotonic
2024-09-20T03:19:37.636406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192000 37
 
0.1%
240000 37
 
0.1%
210000 30
 
0.1%
180000 26
 
0.1%
195000 25
 
0.1%
160000 24
 
0.1%
225000 23
 
0.1%
203000 19
 
0.1%
188000 18
 
0.1%
168000 18
 
0.1%
Other values (19775) 32576
99.2%
ValueCountFrequency (%)
4000 1
< 0.1%
29493 1
< 0.1%
31429 1
< 0.1%
31875 1
< 0.1%
31893 1
< 0.1%
33750 2
< 0.1%
33900 1
< 0.1%
34560 1
< 0.1%
37500 1
< 0.1%
37640 1
< 0.1%
ValueCountFrequency (%)
517810 1
< 0.1%
517125 2
< 0.1%
516893 1
< 0.1%
516760 1
< 0.1%
515960 1
< 0.1%
515867 2
< 0.1%
515703 1
< 0.1%
515680 1
< 0.1%
513440 1
< 0.1%
513000 1
< 0.1%

genre_clean
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3886943
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-09-20T03:19:37.776285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7061076
Coefficient of variation (CV)0.50347048
Kurtosis-1.2602321
Mean3.3886943
Median Absolute Deviation (MAD)1
Skewness0.079062731
Sum111261
Variance2.910803
MonotonicityNot monotonic
2024-09-20T03:19:37.897709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 6043
18.4%
2 5746
17.5%
3 5507
16.8%
4 5431
16.5%
5 5155
15.7%
6 4951
15.1%
ValueCountFrequency (%)
1 6043
18.4%
2 5746
17.5%
3 5507
16.8%
4 5431
16.5%
5 5155
15.7%
6 4951
15.1%
ValueCountFrequency (%)
6 4951
15.1%
5 5155
15.7%
4 5431
16.5%
3 5507
16.8%
2 5746
17.5%
1 6043
18.4%

Interactions

2024-09-20T03:19:21.411915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:01.370312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:02.858672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:04.330679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:06.403731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:08.434472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:10.207833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:11.830400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:13.523093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:15.064959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:16.560153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:18.081353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:19.855617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:21.526433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:01.499696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:02.970711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:04.471222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:06.509228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:08.577020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:10.320662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:11.999166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:13.634759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:15.176848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:16.686209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:18.189213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:19.976686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:21.636256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:01.612138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:03.074398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:04.661981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:06.635193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:08.711925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:10.431842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:12.159863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:13.749369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:15.298215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:16.816656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:18.305667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:20.093172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:21.777676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:01.738001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:03.182939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:04.820222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:06.754233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:08.878671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:10.644188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:12.309760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:13.888276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:15.408745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:16.932039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:18.665076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:20.208927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:21.898265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:01.852040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:03.286112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:05.044774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:06.860232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:09.015212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:10.766410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:12.473085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:14.017128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:15.521883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:17.047343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:18.814518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:20.321122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:22.141695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:01.961680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:03.390019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:05.205054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:06.960894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:09.128742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:10.880087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:12.608164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:14.135550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:15.628965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:17.163111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:18.933018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:20.433224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:22.312778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:02.069236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:03.491732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:05.378193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:07.103946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:09.237532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:10.986712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:12.723072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:14.245285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:15.735530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:17.276997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:19.046152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:20.547119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:22.431904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:02.185642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:03.630167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:05.539382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:07.292158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:09.349098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:11.107977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:12.848046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:14.359749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:15.846616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:17.393002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:19.164293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:20.664292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:22.557462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:02.295773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:03.749802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:05.656036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:07.585086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:09.558703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:11.214823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:12.958551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:14.476959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:15.962219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:17.511888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:19.281041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:20.810804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:22.674946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:02.411683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:03.857205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:05.978879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:07.771987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:09.735650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:11.325202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:13.071913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:14.594473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:16.073744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:17.621956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:19.396755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:20.931367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:22.823632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:02.523294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:03.989159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:06.088771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:07.967975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:09.863848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:11.438533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:13.181861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:14.712987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:16.194422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:17.731533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:19.512878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:21.051693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:22.977764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:02.632158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:04.106675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:06.194977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:08.134302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:09.979851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:11.551240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:13.297654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:14.826208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:16.329671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:17.851600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:19.624730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:21.174984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:23.127467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:02.744648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:04.213693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:06.300620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:08.279364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:10.094187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:11.692216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:13.408873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:14.949712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:16.443773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:17.971044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:19.736618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T03:19:21.298283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-20T03:19:38.011052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
acousticnessdanceabilityduration_msenergygenre_cleaninstrumentalnesskeylivenessloudnessmodeplaylist_genreplaylist_subgenrespeechinesstempotrack_popularityvalence
acousticness1.0000.104-0.075-0.4910.127-0.2140.007-0.058-0.2840.0300.1300.1350.029-0.1670.1290.089
danceability0.1041.000-0.091-0.142-0.173-0.0560.014-0.138-0.0230.0610.2160.1740.261-0.1680.0650.331
duration_ms-0.075-0.0911.000-0.0110.1610.0780.013-0.040-0.1330.0280.1420.164-0.125-0.016-0.121-0.004
energy-0.491-0.142-0.0111.000-0.1010.1150.0090.1400.6560.0260.1870.1770.0650.177-0.1180.123
genre_clean0.127-0.1730.161-0.1011.000-0.085-0.007-0.036-0.1740.1251.0001.000-0.214-0.0860.0990.217
instrumentalness-0.214-0.0560.0780.115-0.0851.0000.012-0.029-0.1620.0050.1410.142-0.1990.071-0.193-0.160
key0.0070.0140.0130.009-0.0070.0121.0000.000-0.0040.3020.0620.0530.029-0.016-0.0010.018
liveness-0.058-0.138-0.0400.140-0.036-0.0290.0001.0000.0830.0130.0520.0630.0570.033-0.030-0.054
loudness-0.284-0.023-0.1330.656-0.174-0.162-0.0040.0831.0000.0150.1240.1280.1000.1120.0650.042
mode0.0300.0610.0280.0260.1250.0050.3020.0130.0151.0000.1250.1500.0660.0300.0290.000
playlist_genre0.1300.2160.1420.1871.0000.1410.0620.0520.1240.1251.0001.0000.2020.2040.1080.127
playlist_subgenre0.1350.1740.1640.1771.0000.1420.0530.0630.1280.1501.0001.0000.1650.1780.1500.124
speechiness0.0290.261-0.1250.065-0.214-0.1990.0290.0570.1000.0660.2020.1651.0000.0260.0070.078
tempo-0.167-0.168-0.0160.177-0.0860.071-0.0160.0330.1120.0300.2040.1780.0261.000-0.022-0.063
track_popularity0.1290.065-0.121-0.1180.099-0.193-0.001-0.0300.0650.0290.1080.1500.007-0.0221.0000.037
valence0.0890.331-0.0040.1230.217-0.1600.018-0.0540.0420.0000.1270.1240.078-0.0630.0371.000

Missing values

2024-09-20T03:19:23.352987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-20T03:19:23.846442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-20T03:19:24.170744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

track_idtrack_nametrack_artisttrack_popularitytrack_album_idtrack_album_nametrack_album_release_dateplaylist_nameplaylist_idplaylist_genreplaylist_subgenredanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_msgenre_clean
06f807x0ima9a1j3VPbc7VNI Don't Care (with Justin Bieber) - Loud Luxury RemixEd Sheeran662oCs0DGTsRO98Gh5ZSl2CxI Don't Care (with Justin Bieber) [Loud Luxury Remix]2019-06-14Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.7480.9166-2.63410.05830.10200.0000000.06530.518122.0361947543
10r7CVbZTWZgbTCYdfa2P31Memories - Dillon Francis RemixMaroon 56763rPSO264uRjW1X5E6cWv6Memories (Dillon Francis Remix)2019-12-13Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.7260.81511-4.96910.03730.07240.0042100.35700.69399.9721626003
21z1Hg7Vb0AhHDiEmnDE79lAll the Time - Don Diablo RemixZara Larsson701HoSmj2eLcsrR0vE9gThr4All the Time (Don Diablo Remix)2019-07-05Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.6750.9311-3.43200.07420.07940.0000230.11000.613124.0081766163
375FpbthrwQmzHlBJLuGdC7Call You Mine - Keanu Silva RemixThe Chainsmokers601nqYsOef1yKKuGOVchbsk6Call You Mine - The Remixes2019-07-19Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.7180.9307-3.77810.10200.02870.0000090.20400.277121.9561690933
41e8PAfcKUYoKkxPhrHqw4xSomeone You Loved - Future Humans RemixLewis Capaldi697m7vv9wlQ4i0LFuJiE2zsQSomeone You Loved (Future Humans Remix)2019-03-05Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.6500.8331-4.67210.03590.08030.0000000.08330.725123.9761890523
57fvUMiyapMsRRxr07cU8EfBeautiful People (feat. Khalid) - Jack Wins RemixEd Sheeran672yiy9cd2QktrNvWC2EUi0kBeautiful People (feat. Khalid) [Jack Wins Remix]2019-07-11Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.6750.9198-5.38510.12700.07990.0000000.14300.585124.9821630493
62OAylPUDDfwRGfe0lYqlCQNever Really Over - R3HAB RemixKaty Perry627INHYSeusaFlyrHSNxm8qHNever Really Over (R3HAB Remix)2019-07-26Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.4490.8565-4.78800.06230.18700.0000000.17600.152112.6481876753
76b1RNvAcJjQH73eZO4BLABPost Malone (feat. RANI) - GATTÜSO RemixSam Feldt696703SRPsLkS4bPtMFFJes1Post Malone (feat. RANI) [GATTÜSO Remix]2019-08-29Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.5420.9034-2.41900.04340.03350.0000050.11100.367127.9362076193
87bF6tCO3gFb8INrEDcjNT5Tough Love - Tiësto Remix / Radio EditAvicii687CvAfGvq4RlIwEbT9o8IavTough Love (Tiësto Remix)2019-06-14Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.5940.9358-3.56210.05650.02490.0000040.63700.366127.0151931873
91IXGILkPm0tOCNeq00kCPaIf I Can't Have You - Gryffin RemixShawn Mendes674QxzbfSsVryEQwvPFEV5IuIf I Can't Have You (Gryffin Remix)2019-06-20Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.6420.8182-4.55210.03200.05670.0000000.09190.590124.9572530403
track_idtrack_nametrack_artisttrack_popularitytrack_album_idtrack_album_nametrack_album_release_dateplaylist_nameplaylist_idplaylist_genreplaylist_subgenredanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_msgenre_clean
328230coMLoVcagZPGF5zxc5RF8Everybody Is In The Place - Radio EditHardwell281PdMbB6qgSzS9zcT9xP6KxEverybody Is In The Place (Radio Edit)2014-04-18♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.6090.9892-3.51010.08670.0004340.2190000.07150.0358130.0461716971
328243zKST4nk4QJE77oLjUZ0NgHey BrotherAvicii2002h9kO2oLKnLtycgbElKswTrue2013-01-01♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.5450.7807-4.86700.04360.0309000.0000460.08280.4580125.0142550931
328252EpS5TgdngSISM63rhBsnKBooyah - Radio EditShowtek470Dix8CfvtZEHUyJGnmPnaBBooyah2013-01-01♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.5590.91611-3.05010.06260.0453000.0000130.22500.1950128.0122152951
328261EavLSmwRWtmkKEmlCfFzTWastedTiësto47584m4QL0kmpG69zSpMKvv8Wasted2014-04-22♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.6450.8322-5.59510.02940.0010600.0026400.19900.3750112.0281883711
328270aBDrRTgDCwWbcOnEIp7DJMany Ways - Radio EditFerry Corsten feat. Jenny Wahlstrom2759XOfNjuYZB6feC6QUzS3eMany Ways2013♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.5810.6405-8.36710.03650.0266000.0000000.57200.2880128.0011969931
328287bxnKAamR3snQ1VGLuVfC1City Of Lights - Official Radio EditLush & Simon422azRoBBWEEEYhqV6sb7JrTCity Of Lights (Vocal Mix)2014-04-28♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.4280.9222-1.81410.09360.0766000.0000000.06680.2100128.1702043751
328295Aevni09Em4575077nkWHzCloser - Sultan & Ned Shepard RemixTegan and Sara206kD6KLxj7s8eCE3ABvAyf5Closer Remixed2013-03-08♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.5220.7860-4.46210.04200.0017100.0042700.37500.4000128.0413531201
328307ImMqPP3Q1yfUHvsdn7wEoSweet Surrender - Radio EditStarkillers140ltWNSY9JgxoIZO4VzuCa6Sweet Surrender (Radio Edit)2014-04-21♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.5290.8216-4.89900.04810.1080000.0000010.15000.4360127.9892101121
328312m69mhnfQ1Oq6lGtXuYhgXOnly For You - Maor Levi RemixMat Zo151fGrOkHnHJcStl14zNx8JyOnly For You (Remixes)2014-01-01♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.6260.8882-3.36110.10900.0079200.1270000.34300.3080128.0083674321
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